Improved Dewatering of Mature Fine Tailings Using High Molecular Weight Polyacrylamide Grafted From an Activated Carbon Surface by Surface‐Initiated <scp>ATRP</scp>
Bibliographic record
Abstract
ABSTRACT The remediation of mature fine tailings remains a critical challenge in oil sand processing due to the inability to dewater the tailings effectively. In this study, high molecular weight polyacrylamide (PAM) (106 g/mol) was grafted from the surface of hydrophobic activated carbon using surface‐initiated atom transfer radical polymerization (SI‐ATRP) to enhance tailings dewatering. Grafting was done using two SI‐ATRP methods, standard and activators regenerated by electron transfer, to evaluate each method's ability to form high molecular weight brushes from a prefunctionalized activated carbon surface that was oxidized and attached with ATRP initiators. Size exclusion chromatography showed that the grafted PAM brushes achieved molecular weights greater than 106 g/mol, while thermogravimetric analysis showed that they had activated carbon contents of 0.5–5.8 wt%. The dewatering performance of the resulting high molecular weight PAM from activated carbon was evaluated against neat PAM by performing settling tests on dilute mature fine tailings. In summary, this work demonstrates the successful grafting of high molecular weight PAM from activated carbon, improving the dewatering of mature fine tailings shown by the increase of solids content up to 50 wt% compared to neat PAM, which only reached 20 wt%. This work advances the application of hybrid flocculants and paves the way for improved water recovery and sustainable tailings management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".